How does computer-directed statistical filtering improve inertial navigation equipment?

Computer-directed statistical filtering, specifically through the use of Kalman filtering, significantly improves the accuracy of inertial navigation systems by optimizing how data is processed.

Because inertial sensors like gyroscopes and accelerometers can accumulate minute errors over time, relying on them alone can lead to significant navigational drift. Statistical filtering addresses this by:

• Weighting Data Quality: The computer assigns weights to various incoming data streams—such as inertial outputs, Doppler-effect measurements, or GPS signals—based on their expected reliability and precision.1
• Estimating Current State: By integrating these weighted inputs with previous position and velocity solutions, the system produces a more accurate "best estimate" of the vehicle's current location and trajectory than any single sensor could provide alone.1

This process allows modern digital computers to continuously refine navigational data, effectively mitigating the inherent inaccuracies of individual sensors and ensuring more precise control of the vehicle's path.21